Hyperspectral image is well-known for the identification of the objects on the earth's surface. Most of the classifier uses the spectral features and does not consider the spatial features to perform the classification and to recognize the various objects on the image. In this paper, the hyperspectral image is classified based on spectral and spatial features using a convolutional neural network (CNN). The hyperspectral image is divided into a small number of patches. CNN constructs the high level spectral and spatial features of each patch, and the multilayer perceptron helps in the classification of image features into different classes. Simulation results show that CNN archives the highest classification accuracy of the hyperspectral image compared with other classifiers.

Hyperspectral images are used to identify and detect the objects on the earth's surface. Classifying of these hyperspectral images is becoming a difficult task, due to more number of spectral bands. These high dimensionality problems are addressed using feature reduction and extraction techniques. However, there are many challenges involved in the classification of data with accuracy and computational time. Hence in this paper, a method has been proposed for hyperspectral image classification based on support vector machine (SVM) along with guided image filter and principal component analysis (PCA). In this work, PCA is used for the extraction and reduction of spectral features in hyperspectral data. These extracted spectral features are classified using SVM like vegetation fields, building, etc., with different kernels. The experimental results show that SVM with Radial Basis Functions (RBF) kernel will give better classification accuracy compared to other kernels. Moreover, classification accuracy is further improved with a guided image filter by incorporating spatial features.

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Abstract: Historically, travelling wave tube amplifier (TWTA) has been a common type of Microwave amplifier used commonly in terrestrial and space application due to their high efficiency and power handling capacity. However due to their bulky nature and also being very expensive, it is difficult to use them commercially in a large scale. F amplifier depends on how many harmonics are used for the amplification process. Here, the amplification process is performed up to the third harmonics which provides 41.606 dBm output power with 27dBm input power. Also a gain of more than 20.277dBm is achieved when the input given is 27dBm. Several other results like reflection Coefficient and transmission coefficient simulations has also been provided with the power added efficiency (PAE) of 75.402 achieved has also been simulated. Inspired by the advantage such as very less development cost, minimum supply voltage, gradual degradation and numerous commercial applications, Solid State Power Amplifier (SSPA) has been the replacement to vacuum tube Technology. The efficiency of the amplifier is one of the most important task in the microwave engineering research. An important figure of merit, power-added efficiency (PAE), is the main focus. Hence in this paper, class F Power amplifier is designed for 2.4GHz frequency. Class F Amplifier is also called as wave shaping amplifier since the harmonics generated helps the amplification process. The class f PA is biased nearer to the class B amplifier (close cut-off area) so the transistor can move back and forth rapidly to produce the harmonics. The efficiency of class